While the ability to hijack and maliciously override a self-driving car may seem like the plot of a movie, with recent innovations in artificial intelligence, this is the world we’re living in. In fact, researchers recently discovered methods to perturb the appearance of a stop sign so that an autonomous vehicle would classify it as a merge or speed limit sign. Scary, right?
That’s why adversarial images and prompts are posing increasing threats to AI. Stress testing AI models is no longer a futuristic fantasy; it’s a complete necessity, with many regulatory frameworks now mandating it.
AI red teaming and pentesting are the most effective methods for securing your models against real-world threats, and in this blog, we’ll define adversarial testing, explain its importance and outline the testing lifecycle.
What is adversarial testing in AI?
Adversarial testing in AI is the process of intentionally trying to break machine learning models with harmful or malicious inputs.
This includes both explicit adversarial prompts, which are direct and clearly designed to fool the model, and implicit adversarial prompts, which subtly manipulate inputs to cause unexpected or incorrect behaviour.
This extends to fooling even deep learning algorithms, which can pose serious security risks to your business.
Both pose an equal threat to the use of generative AI in your workplace, and security professionals must stay vigilant against this malicious attack method to prevent adversarial attacks.
Adversarial vs Standard Testing
Adversarial testing differs from traditional security testing, which uses static logic and known attack paths to review your security posture. It takes a deterministic approach to decide whether your security testing passes or fails. While this can be effective for identifying vulnerabilities in some security environments, it fails to account for AI models and is not particularly effective at testing them
Here’s a breakdown of how AI adversarial testing targets AI attacks specifically:
| Aspect | Traditional Security Testing | AI Adversarial Testing |
|---|---|---|
| Testing Logic | Static logic with predefined test cases | Probabilistic behaviour with adaptive scenarios |
| Attack Paths | Known, documented attack vectors | Unknown, emergent failure modes |
| Results | Deterministic pass/fail outcomes | Distribution-based risk assessment |
| Approach | Rule-based with expected inputs/outputs | Exploits model uncertainties and edge cases |
Why adversarial testing matters
Adversarial testing is crucial because manual methods can’t keep up with the rapidly expanding AI attack surface, driven by evolving tools, RAG, and multimodal models that increase complexity and vulnerabilities. Key reasons for its importance include:
It reveals core business risks
- AI adversarial testing reveals vulnerabilities: unsafe outputs, prompt injection, data leakage, hallucination abuse, bias and fairness risks, tool misuse and agent escalation can all be identified.
- However, many AI teams deploy systems without adversarial testing and discover vulnerabilities only after incidents. A proactive approach is key to staying ahead of data poisoning and bad actors.
It’s essential in meeting compliance
- Regulations increasingly encourage or require adversarial testing for high-risk AI systems, recognising its critical role in identifying vulnerabilities and ensuring compliance with safety standards.
- Frameworks such as the EU AI Act and NIST AI Risk Management Framework (AI RMF) are setting emerging compliance expectations that emphasise rigorous adversarial testing to mitigate risks associated with AI deployment.
Adversarial testing limitations
As much as security professionals wish it were, adversarial testing is not a sole solution against potential attacks. This is due to three key reasons:
- Limited coverage of infinite prompt space: Because users can interact with AI models in potentially infinite ways, adversarial testing cannot cover every possible input or scenario, leaving some vulnerabilities undiscovered.
- Quality varies between automated and manual testing: Automated adversarial testing tools may lack the nuance and creativity of human testers, resulting in less realistic or comprehensive attack simulations, while manual testing can be time-consuming and resource-intensive.
- Human attackers behave differently from simulated ones: Real-world attackers may exploit unexpected tactics or combine multiple attack vectors in ways that automated or scripted adversarial tests do not anticipate, making it difficult to fully replicate their behaviour during testing.
AI red teaming as an adversarial testing methodology
It’s important to note that AI red teaming is not the same as traditional red teaming. It has three approaches: manual, automated, and hybrid. Here’s how they differ:
| Approach | How It Works | Strengths | Limitations |
|---|---|---|---|
| Manual | Human experts craft adversarial prompts by hand | Discovers novel, nuanced failure modes using creativity and domain expertise | Time-intensive, limited scale |
| Automated | Algorithms generate thousands of adversarial inputs automatically | Fast, scalable testing across vast input spaces | May miss context-dependent vulnerabilities |
| Hybrid | Combines human expertise with automated tools | Balances thoroughness with efficiency; humans identify vectors, automation scales testing | Requires coordination between tools and testers |
For more information on how AI red teaming works to target machine learning models, check out our AI red teaming guide.
The adversarial testing lifecycle
Understanding the adversarial testing workflow is core to effectively mitigating AI-related risks. Below is a general overview of a typical adversarial testing lifecycle, helping organisations to understand how testing interplays with business operations.
Threat modelling and risk scoping
Threat modelling identifies realistic misuse and failure scenarios across the AI lifecycle. It helps your security team to identify attack surfaces, map misuse scenarios, and define harm categories
Attack design
Attack design in adversarial testing encompasses techniques such as prompt engineering attacks, multi-turn attacks, context manipulation, and data exfiltration tests to evaluate and exploit vulnerabilities in AI models. With concise attack designs, you can ensure your adversarial environments are well-targeted and effective by the test that follows, rather than shooting in the dark at vague potential threats.
Test execution
Test execution is the critical phase in the adversarial testing lifecycle where the designed attacks are actively carried out through manual red-team exercises, automated fuzzing, and continuous evaluation to identify weaknesses and assess the AI model’s resilience in realistic adversarial environments.
Here, the majority of security gaps will be identified, allowing your security team to evaluate sensitive attributes within your datasets.
Result analysis and risk scoring
Once you have received the results of your adversarial red-teaming programme, you must rank these vulnerabilities by severity and business impact. A popular framework for accurately ranking each threat is the Common Vulnerability Scoring System (CVSS), which is a standardised framework for scoring IT vulnerabilities from 1 to 10 – 1 being low and 10 being critical.
Remediation and hardening
Post-test, remediation and hardening are essential.
- Enforce guardrails to secure model outputs and protect against data poisoning.
- Model tuning supports improving the AI model’s resilience by adjusting parameters and retraining with adversarial examples to better handle malicious inputs and reduce vulnerabilities.
- Governance and the addition of a human observer to oversee your LLM minimises the risk of the model automatically learning malicious samples.
Continuous testing / CI integration
Adversarial testing isn’t a one-time thing: to be truly effective, it should be embedded into development pipelines and monitoring cycles. To save your board from paying twice for testing, invest in vendors like OnSecurity, which offer a free retest service with any security test.
This way, you can certify the efficacy of your remediations and quickly plug any remaining security gaps without burning through your budget.
Common AI Adversarial Attack Methods
| Attack Layer | Attack Type | What It Does |
|---|---|---|
| Input Layer | Jailbreaks | Bypasses safety filters to get harmful responses |
| Prompt Injection | Hides malicious instructions in normal-looking inputs | |
| Obfuscated Instructions | Uses encoding (Base64, etc.) to evade content filters | |
| Retrieval/Middleware Layer | RAG Poisoning | Poisons training dataset sources so the AI retrieves false information |
| Context Override | Replaces system instructions with the attacker’s commands | |
| Model Behaviour Layer | Harmful Output Generation | Tricks the model into producing prohibited content |
| Hallucinated Sensitive Data | Makes the model fabricate confidential information | |
| Data Layer | Training Data Leakage | Extracts memorised data from the model’s training |
| Embedding Inversion | Reconstructs original data from vector embeddings | |
| Agent & Tool Use Layer | Unauthorised Tool Execution | Forces AI agents to run functions without permission |
| Data Exfiltration | Uses APIs to steal and send data externally | |
| Chain-of-Thought Manipulation | Alters reasoning steps to reach malicious outcomes |
Each layer presents unique vulnerabilities requiring targeted testing strategies to ensure your organisation’s AI security is effective.
Other AI adversarial testing methodologies (beyond red teaming)
Beyond red teaming, there is a broad range of alternatives for AI adversarial testing, each targeting a unique aspect of AI security. Some key examples include:
- Benchmark testing – safety metrics and policy violation scoring
- Fuzz testing – Randomised prompt generation
- Human-in-the-loop testing – Real-world UX testing, bias auditing
- AI-assisted testing – Self-adversarial models, synthetic dataset generation
Adversarial testing tools
| Tool Category | Description | Examples |
|---|---|---|
| Vulnerability Scanners | Automated tools that scan AI systems for known vulnerabilities and security misconfigurations | Static analysis tools, dependency checkers, and configuration validators |
| Prompt Fuzzers | Tools that generate adversarial inputs to test model robustness and identify prompt injection vulnerabilities | Automated prompt generation, mutation-based testing, boundary testing |
| Evaluation Frameworks | Frameworks for assessing AI model performance, safety, and alignment | Benchmark suites, safety evaluation toolkits, bias detection frameworks |
| Continuous Monitoring Tools | Real-time monitoring solutions that track AI system behaviour in production and detect anomalies | Runtime monitoring, anomaly detection, behavioural analytics |
| Penetration Testing / Red Teaming Services | Expert-led security testing that simulates real-world attacks to identify AI security risks. | OnSecurity provides specialised AI penetration testing with free retests |
When to engage a red team
When you choose to engage a red team is just as critical as testing your AI systems themselves. Key triggers that should encourage you to conduct LLM red teaming include:
- Pre-production release: proactive testing is always best.
- After model architecture change
- Before regulatory certification
- Post security incident
With OnSecurity’s LLM and AI red teaming, organisations can rest assured their assets are well-defended against targeted attacks.
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